Abstract

Whether a person is in company is an important indicator for several research fields such as monitoring a patient’s mental health states in clinical psychology or interruptibility detection in experience sampling. Traditionally, social activity is assessed using self-report questionnaires. However, this approach is obtrusive. The best solution would be an automatic assessment. Smartphones are suitable sensing systems for this task. In this paper, we investigate relations between being in company and place types. First, we present results of an online survey taken by 68 persons. Within the survey, we assessed how likely users are to be in company at specific place types provided by the Google Places API. We identified that places such as night club, bar, movie theatre, and restaurant are primarily visited in company. Places such as post office, gym, bank, or library are visited rather alone. Some place types are undecidable and require additional context information. As a next step, we ran an in-field user study to gather enriched real-world data. We logged temporal features, user activity, place type, and self-reported company indicators as ground truth. We gathered data of 24 participants over a period of three weeks. Using information gain and \(\chi ^2\), we identified that place type and hour of day correlate with being in company with statistical significance shown by Cramér’s V. Using machine learning, we trained different classifiers to predict being in company. We achieved an accuracy of up to 91.1%. Our approach is a first step towards an automatic assessment of being in company as it is more accurate than pure guessing. We propose to enrich it with further context information such as transportation mode or a more accurate activity classifier.